Predictive Analytics in System Reliability
This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning relia...
Sábháilte in:
| Údar corparáideach: | |
|---|---|
| Rannpháirtithe: | , |
| Formáid: | Leictreonach Ríomhleabhar |
| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Eagrán: | 1st ed. 2023. |
| Sraith: | Springer Series in Reliability Engineering,
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| Ábhair: | |
| Rochtain ar líne: | https://doi.org/10.1007/978-3-031-05347-4 |
| Clibeanna: |
Cuir clib leis
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-05347-4 | ||
| 003 | DE-He213 | ||
| 005 | 20260810170647.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220908s2023 sz | o |||| 0|eng d | ||
| 020 | |a 9783031053474 |9 978-3-031-05347-4 | ||
| 024 | 7 | |a 10.1007/978-3-031-05347-4 |2 doi | |
| 050 | 4 | |a TA342-343 | |
| 072 | 7 | |a PBWH |2 bicssc | |
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| 072 | 7 | |a PBWH |2 thema | |
| 072 | 7 | |a TBJ |2 thema | |
| 082 | 0 | 4 | |a 003.3 |2 23 |
| 245 | 1 | 0 | |a Predictive Analytics in System Reliability |h [electronic resource] / |c edited by Vijay Kumar, Hoang Pham. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XI, 282 p. 94 illus.). |b 56 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Springer Series in Reliability Engineering, |x 2196-999X | |
| 505 | 0 | |a Chapter 1. Deep Learning Approach for the Reliability Assessment of Cloud and Edge Open Source Software (Hironobu SONE) -- Chapter 2. Parameter Estimation of Software Reliability Growth Models using Mayfly Optimization (Ankur Choudhary) -- Chapter 3. Study of Automation of Test Generation using Genetic Algorithm Approach (Sonali Vyas) -- Chapter 4. Measuring and evaluating big data factors in an enterprise using fuzzy based MOORA and Best Worst Method (Abhishek Srivastava) -- Chapter 5. Reliability assessment and profit analysis of automated teller machine system under copular repair policy (Ibrahim Yusuf) -- Chapter 6. Entropy-Based Mathematical Modeling for Software Evolution (V.B.Singh).-Chapter 7. Development of Reliability Block Diagram (RBD) Model for Reliability Analysis of a Steam Boiler System (Suyog S. Patil) -- Chapter 8. Prediction of software failure using fuzzy time series method (Mohamed Ashik. A) -- Chapter 9. Big Data Quality Management System (Yury Klochkov).-Chapter 10. A Deep Analysis of Bad Smells using Machine Learning Classifiers (Aakanshi Gupta) -- Chapter 11. Multi-Criterion Intuitionistic Fuzzy-TOPSIS Based Analysis for Software Quality Metric (Adarsh Anand).-Chapter 12. Reliability Allocation and Sensitivity Assessment of Solar Air Conditioner from Markov Process (Akshay Kumar) -- Chapter 13. Design and Development of Question Answering System using Named Entity Recognition (NER) (Abhijit Kumar) -- Chapter 14. Multi-attribute utility functions based strategic decision for software release time optimization (Vishal Pradhan) -- Chapter 15. Empirical Evaluation of Code Bad Smells in Open-Source Software using Machine learning technique (Stuti Tandon) -- Chapter 16. Reliability optimization of a system stand by system using PSO technique (Mangey Ram) -- Chapter 17. Probabilistic model for the availability analysis of the milling section of rice mill plant (Ibrahim Yusuf) -- Chapter 18. An Opportunistic Maintenance Model of Load Sharing Systems with PM and Minimal Repairs (Maryam Arabzadeh). | |
| 520 | |a This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering. The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject. | ||
| 588 | |a Print version record. | ||
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Financial risk management. | |
| 650 | 0 | |a Industrial Management. | |
| 650 | 1 | 4 | |a Mathematical Modeling and Industrial Mathematics. |
| 650 | 2 | 4 | |a Risk Management. |
| 650 | 2 | 4 | |a Industrial Management. |
| 700 | 1 | |a Kumar, Vijay. |e editor. |0 (orcid)0000-0002-2996-7181 |1 https://orcid.org/0000-0002-2996-7181 |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Pham, Hoang. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031053467 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031053481 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031053498 |
| 830 | 0 | |a Springer Series in Reliability Engineering, |x 2196-999X | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-05347-4 |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||